Computers are making leap developments and are used in various scenes in the society these days. However, these computers called Neumann types are very weak in processing (e.g., real-time human face recognition) easy for a human because of their characteristics in processing schemes themselves.
To cope with such processing, research has been done on neural networks as operation processing models which mimic the information processing scheme of the brain.
As a model of neurons which form a neural network, generally, output values from a plurality of units (neurons) are weighted by a synaptic weight, and the products are input to a unit corresponding to a neuron. The sum of input values is further nonlinearly converted and output. That is, in a general neural network, desired processing is realized by product-sum operation and nonlinear conversion in each unit and between units.
As neural network architectures using the neuron model, associative memories which connect units that execute the product-sum operation to each other or pattern recognition models which hierarchically connect units that execute the product-sum operation have been proposed conventionally.
To put a neural network into practical use and form it as an integrated circuit, the product-sum operation must be executed more efficiently. Especially, the efficiency is necessary in the execution speed of operation and power consumption.
Various proposals have been made in association with the neuron models and neural network architectures which execute the product-sum operation. For example, Japanese Patent Laid-Open No. 05-210651 discloses a method of executing a product-sum operation to form a hierarchical neurocomputer.